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Abstract Human language relies on the correct processing of syntactic information, as it is essential for successful communication between speakers. As an abstract level of language, syntax has often been studied separately from the physical form of the speech signal, thus often masking the interactions that can promote better syntactic processing in the human brain. However, behavioral and neural evidence from adults suggests the idea that prosody and syntax interact, and studies in infants support the notion that prosody assists language learning. Here we analyze a MEG dataset to investigate how acoustic cues, specifically prosody, interact with syntactic representations in the brains of native English speakers. More specifically, to examine whether prosody enhances the cortical encoding of syntactic representations, we decode syntactic phrase boundaries directly from brain activity, and evaluate possible modulations of this decoding by the prosodic boundaries. Our findings demonstrate that the presence of prosodic boundaries improves the neural representation of phrase boundaries, indicating the facilitative role of prosodic cues in processing abstract linguistic features. This work has implications for interactive models of how the brain processes different linguistic features. Future research is needed to establish the neural underpinnings of prosody-syntax interactions in languages with different typological characteristics.

Over the last two decades, an emerging body of research has demonstrated that non-human animals exhibit the ability to combine context-specific calls into larger sequences. These structures have frequently been compared with language’s syntax, whereby linguistic units are combined to form larger structures, and leveraged to argue that syntax might not be unique to language. Currently, however, the overwhelming majority of examples of call combinations are limited to simple sequences comprising just two calls which differ dramatically from the open-ended hierarchical structuring of the syntax found in language. We revisit this issue by taking a whole-repertoire approach to investigate combinatoriality in common marmosets (Callithrix jacchus). We use Markov chain models to quantify the vocal sequences produced by marmosets providing evidence for structures beyond the bigram, including three-call and even combinations of up to eight or nine calls. Our analyses of these longer vocal sequences are suggestive of potential further internal organization, including some amount of recombination, nestedness and non-adjacent dependencies. We argue that data-driven, whole-repertoire analyses are fundamental to uncovering the combinatorial complexity of non-human animals and will further facilitate meaningful comparisons with language’s combinatoriality.

Background: One of the most widely used instruments for assessing agitation in dementia patients is the Cohen-Mansfield Agitation Inventory (CMAI), nevertheless no global score has been proposed. The aim of this study is: (a) to conduct a confirmatory (CFA) and exploratory factor analysis (EFA) of CMAI on people with dementia and Psychological and Behavioral Symptoms (BPSD), and (b) to propose an alternative structure, based on clinical criteria including all CMAI items. Methods: Confirmatory and exploratory factor analyses were carried out on the CMAI 29 items administered at baseline to 505 patients with dementia (PwD) and BPSD enrolled in the international observational RECage study. Results: The three-factor structure has not been confirmed by the CFA, whilst the EFA was carried out respectively on 25 items disregarding 4 items with a prevalence ≤5% and then on 20 items disregarding 9 items with a prevalence ≤10%. The four-factor structure explaining 56% of the variance comprised Physically Aggressive behavior, Verbally Aggressive behavior, Physically non-aggressive behavior, and Physically and verbally aggressive behavior. Conclusions: A new grouping of all items according to a clinical criterion is proposed, allowing for a more sensible evaluation of the symptoms leading to better differentiation.

To process language in a way that is compatible with human expectations in a communicative interaction, we need computational representations of lexical properties that form the basis of human knowledge of words. In this article, we concentrate on word-level semantics. We discuss key concepts and issues that underlie the scientific understanding of the human lexicon: its richly structured semantic representations, their ready and continual adaptability, and their grounding in crosslinguistically valid conceptualization. We assess the state of the art in natural language processing (NLP) in achieving these identified properties, and suggest ways in which the language sciences can inspire new approaches to their computational instantiation.

Clefts are understood as biclausal structures involving the movement of a clefted constituent from a lower clause, where it is generated, to a higher clause, where it is interpreted. Though both grammatical, subject and object clefts show signs of different acceptability in experimental settings. This degradation is ascribed to the fact that the object needs to cross an intervening subject, thus triggering intervention effects. In this paper, we show that intervention effects are also present in grammatical configurations, and give rise to lower-than-expected frequencies. Based on sets of features that play a role in the syntactic computation of locality, we compare the theoretically expected and the actually observed counts of features in a corpus of thirteen syntactically annotated treebanks for three languages (English, French, Italian). We find the quantitative effects predicted by the theory of intervention locality: object clefts are less frequent than expected in intervention configuration, while subject clefts are roughly as frequent as expected. We also find that the size of the effect is proportional to the number of features that give rise to the intervention effect. These results provide a three-fold contribution. First, they extend the empirical evidence in favour of the feature-based intervention theory of locality. Second, they provide theory-driven quantitative evidence, thus extending in a novel way the sources of evidence used to adjudicate theories. Finally, the paper provides a blueprint for future theory-driven quantitative investigations.

In this paper, we illustrate a novel method to translate a derivational explanation of Universal 20 into vectorial representations. We exploit this vectorial representation to answer a number of theoretical questions. First, we use linear regression to automatically rank the costs of different syntactic movements within this proposal and investigate some proposals on partial and complete movement. This investigation of movement suggests that the nature of the movement is important, while the importance of harmonic specification of functional categories, i.e. whether the movement is partial or complete, is more context-dependent. We then evaluate whether the base order dem num adj n is the best predictor of the typological facts. We compare different syntactic proposals on the position of numerals in the noun phrase. We find that a merge position of numerals higher than adjectives has better results in both methods. We also show, using this method, that the independently motivated low merge position for numerals can only be semantically motivated, which results in intra-linguistic variation, and is not a parametric choice.

In the computational study of human intelligence, the language sciences are in the unique position of resting both on sophisticated theories and representations and on large amounts of observational data available for many languages. In this paper, we discuss some recent results, where large-scale, data-intensive computational modelling techniques are used to address fundamental linguistic questions on the quantitative properties of abstract grammatical representations. Specifically, we present a programme of research exemplified in three case studies to identify the causes of frequency differentials. In the area of word order, we discuss work that investigates whether typological and corpus frequencies are systematically correlated to abstract syntactic structures and to higher-level structural principles of minimisation and efficiency. In the area of verb meaning, corpus-based computational models are discussed that investigate how frequencies are correlated to well-known lexical effects in causative alternations and morphological marking. The large corpus-based, cross-linguistic component of the work and the abstract grammatical hypotheses on word order and verb meaning provide new empirical and computational evidence to the important debate on language variation, its extent and its limits and illustrate how to bring corpus-based computational methodology to bear on theoretical syntactic issues. In so doing, we help reduce the current gap between theoretical and computational linguistics.

This paper shows a computational learning paradigm to compare and test theories about language universals. Its main contribution lies in the illustration of the encoding and comparison of theories about typological universals to measure the generalisation ability of these theories. In so doing, this method uncovers hidden dependencies between theoretical dimensions and primitives that were considered independent and independently motivated.

Hypoxia has been associated with several pathological conditions ranging from stroke to cancer. This condition results in the activation of autophagy, a cyto-protective response involving the formation of double-membraned structures, the autophagosomes, in the cytoplasm. In this study, we investigated the cellular mechanisms regulating the autophagy gene Ambra1, after exposure to a hypoxia mimetic, cobalt chloride (CoCl2). We observed that, upon CoCl2 administration, activation of the apoptotic machinery was concomitant with down-regulation of the pro-autophagic factor Ambra1, without affecting transcription. Additionally, co-treating the cells with the caspase inhibitor z-VAD-FMK did not restore Ambra1 protein levels, this implying the involvement of other regulatory mechanisms. Partial re-localization of Ambra1 mRNA to non-translating fractions and cytoplasmic P-bodies was further detected. Thus, in this pseudohypoxic context, Ambra1 mRNA translocation to P-bodies and translational suppression correlated with increased cell death.

The recent considerable growth in the amount of easily available on-line text has brought to the foreground the need for large-scale natural language processing tools for text data mining. In this paper we address the problem of organizing documents into meaningful groups according to their content and to visualize a text collection, providing an overview of the range of documents and of their relationships, so that they can be browsed more easily. We use Self-Organizing Maps (SOMs) (Kohonen 1984). Great efficiency challenges arise in creating these maps. We study linguistically-motivated ways of reducing the representation of a document to increase efficiency and ways to disambiguate the words in the documents.